Simulation of Population-Based Commuter Exposure to NO2 Using Different Air Pollution Models

Abstract

We simulated commuter routes and long-term exposure to traffic-related air pollution during commute in a representative population sample in Basel (Switzerland), and evaluated three air pollution models with different spatial resolution for estimating commute exposures to nitrogen dioxide (NO2) as a marker of long-term exposure to traffic-related air pollution. Our approach includes spatially and temporally resolved data on actual commuter routes, travel modes and three air pollution models. Annual mean NO2 commuter exposures were similar between models. However, we found more within-city and within-subject variability in annual mean (±SD) NO2 commuter exposure with a high resolution dispersion model (40 ± 7 µg m−3, range: 21–61) than with a dispersion model with a lower resolution (39 ± 5 µg m−3; range: 24–51), and a land use regression model (41 ± 5 µg m−3; range: 24–54). Highest median cumulative exposures were calculated along motorized transport and bicycle routes, and the lowest for walking. For estimating commuter exposure within a city and being interested also in small-scale variability between roads, a model with a high resolution is recommended. For larger scale epidemiological health assessment studies, models with a coarser spatial resolution are likely sufficient, especially when study areas include suburban and rural areas.

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

“…Several studies have compared population exposure estimates based on static populations with spatially and temporally dynamic populations, emphasizing the need to account for population dynamics to reduce bias in population exposure estimates [21,[23][24][25][26][36][37][38][39]45,46,[63][64][65][66][67][68][69][70][71][72][73][74][75][76]. Thus, appropriate population exposure estimates in urban areas require the consideration of individual's activities, which have a large degree of spatial and temporal dynamics.…”

Tool for assessing health and equity impacts of interventions modifying air quality in urban environments

“…Spatial distribution here refers to intra-urban exposure heterogeneity and does not encompass the differential distribution that can occur between a city and its surroundings (Ragettli, Tsai, et al, 2014). The following is a selection of examples to illustrate how these determinants may be acted upon in order to modify the spatial distribution.…”

The Influence of Residential and Workday Population Mobility on Exposure to Air Pollution in the UK

“…The magnitude and direction of this bias is widely discussed in the environmental exposure and health effects literature, primarily from the viewpoint of utilising small, portable air pollution sensors to quantify personal exposure directly on an individual level (Steinle et al, 2013Buonanno et al, 2012;Gariazzo et al, 2016;Marek et al, 2016) or mobile devices to assess mobility (Dewulf et al, 2016;Nyhan et al, 2016;Glasgow et al, 2016;Park and Kwan, 2017). While results emerging from these studies are important for understanding the impact of specific mobility patterns (Setton et al, 2008(Setton et al, , 2011Beckx et al, 2009;Dons et al, 2011;Dhondt et al, 2012;Ragettli et al, 2014Ragettli et al, , 2015Brokamp et al, 2016;Smith et al, 2016), for exposure in different micro-environments and for the relative contributions of these to overall personal exposure, up-scaling from this individual level to population level exposure is not straightforward.…”